Implementation. We implement the renderer \(g\)  of primitive capsules using signed distance fields [1]} and their encoder \(\gamma \)  using an AlexNet-like convolutional neural network [2]} for regression. For semantic capsules we use Equations REF -REF  for \(\gamma \)  and a 4-layer deep dense regression neural network with \(\tanh \)  activation functions for \(g\) . The training data is generated synthetically using the process described in this paper and all hyperparameters, such as learning rate, are fine-tuned by hand. Our implementation called VividNet is found on Github at https://github.com/Kayzaks/VividNet.
